metawards.Parameters¶

class metawards.Parameters(input_files: metawards._inputfiles.InputFiles = None, uv_filename: str = None, disease_params: metawards._disease.Disease = None, additional_seeds: List[str] = None, length_day: float = 0.7, plength_day: float = 0.5, initial_inf: int = 5, static_play_at_home: float = 0.0, dyn_play_at_home: float = 0.0, dyn_dist_cutoff: float = 10000000.0, play_to_work: float = 0.0, work_to_play: float = 0.0, local_vaccination_thresh: int = 4, global_detection_thresh: int = 4, daily_ward_vaccination_capacity: int = 5, neighbour_weight_threshold: float = 0.0, daily_imports: float = 0.0, stage_0: str = 'R', UV: float = 0.0, user_params: Dict[str, float] = None, adjustments: List[metawards._variableset.VariableSet] = None, _name: str = None, _version: str = None, _authors: str = None, _contacts: str = None, _references: str = None, _filename: str = None, _repository: str = None, _repository_version: str = None, _repository_branch: str = None, _repository_dir: str = None)[source]¶

The full set of Parameters that are used to control the model outbreak over a Network. The combination of a Network and a Parameters defines the model outbreak.

Load the Parameters using the Parameters.load function, and then add extra data using the various “set” and “add” functions, e.g.

Examples

>>> params = Parameters.load("march29")
>>> params.set_disease("ncov")
>>> params.set_input_files("2011Data")
>>> params.add_seeds("ExtraSeedsBrighton.dat")

__init__(input_files: metawards._inputfiles.InputFiles = None, uv_filename: str = None, disease_params: metawards._disease.Disease = None, additional_seeds: List[str] = None, length_day: float = 0.7, plength_day: float = 0.5, initial_inf: int = 5, static_play_at_home: float = 0.0, dyn_play_at_home: float = 0.0, dyn_dist_cutoff: float = 10000000.0, play_to_work: float = 0.0, work_to_play: float = 0.0, local_vaccination_thresh: int = 4, global_detection_thresh: int = 4, daily_ward_vaccination_capacity: int = 5, neighbour_weight_threshold: float = 0.0, daily_imports: float = 0.0, stage_0: str = 'R', UV: float = 0.0, user_params: Dict[str, float] = None, adjustments: List[metawards._variableset.VariableSet] = None, _name: str = None, _version: str = None, _authors: str = None, _contacts: str = None, _references: str = None, _filename: str = None, _repository: str = None, _repository_version: str = None, _repository_branch: str = None, _repository_dir: str = None) → None¶

Methods

`__init__`(input_files, uv_filename, …)
`add_seeds`(filename)	Add an ‘additional seeds’ file that can be used to seed wards with new infections at different times and locations.
`copy`(include_subparams)	Return a safe copy of these parameters, which does not include any subnetwork parameters if ‘include_subparams’ is False
`default`()	Return the default set of parameters
`get_repository`(repository)	Return the repository location and version information for the passed repository
`load`(parameters, repository, folder, filename)	This will return a Parameters object containing all of the parameters loaded from the parameters found in file f”{repository}/{folder}/{parameters}.json”
`read_variables`(filename, line_numbers)	Read in extra variable parameters from the specified line number(s) of the specified file, returning the list of the dictionaries of variables that have been read.
`set_disease`(disease, silent)	“Set the disease that will be modelled
`set_input_files`(input_files)	Set the input files that are used to initialise the simulation
`set_variables`(variables)	This function sets the adjustable variable values to those specified in ‘variables’ in A COPY OF THIS PARAMETERS OBJECT.
`specialised_demographics`()	Return the names of demographics that have specialised parameters that are different to those of the overall network

Attributes

`UV`	Seasonality parameter
`additional_seeds`	The set of files that contain additional seeds that seed the outbreak during the model run
`adjustments`	All of the VariableSet adjustments that have been applied to these parameters
`daily_imports`	proportion of daily imports
`daily_ward_vaccination_capacity`
`disease_params`	The set of parameters that define the disease
`dyn_dist_cutoff`
`dyn_play_at_home`
`global_detection_thresh`
`initial_inf`	The number of initial infections
`input_files`	The set of input files that define the model Network
`length_day`	The fraction of day considered “day” for work, e.g.
`local_vaccination_thresh`
`neighbour_weight_threshold`
`play_to_work`
`plength_day`	The fraction of day considered “day” for play
`stage_0`	how to treat the * state (stage 0).
`static_play_at_home`
`user_params`	User parameters
`uv_filename`	The name of the UV file
`work_to_play`

UV = 0.0¶: Seasonality parameter

__getitem__(demographic: str)[source]¶: Return the parameters that should be used for the demographic subnetwork called ‘demographic’. If these have not been set specifically then the parameters for the overall network are used

__str__()[source]¶: Return str(self).

add_seeds(filename: str)[source]¶

Add an ‘additional seeds’ file that can be used to seed wards with new infections at different times and locations. Several additional_seed files can be added

Parameters:	filename (str) – Name of the file containing the additional seeds

additional_seeds = None¶: The set of files that contain additional seeds that seed the outbreak during the model run

adjustments = None¶: All of the VariableSet adjustments that have been applied to these parameters

copy(include_subparams: bool = False)[source]¶: Return a safe copy of these parameters, which does not include any subnetwork parameters if ‘include_subparams’ is False

daily_imports = 0.0¶: proportion of daily imports

static default()[source]¶: Return the default set of parameters

disease_params = None¶: The set of parameters that define the disease

static get_repository(repository: str = None)[source]¶

Return the repository location and version information for the passed repository

Parameters:	repository (str) – Location on the filesystem of the repository. If this is None then it will be searched for using first the environment variable METAWARDSDATA, then $HOME/GitHub/MetaWardsData, then ./METAWARDSDATA
Returns:	(repository, version) – A tuple of the location on disk of the repository, plus the version information (git ID etc)
Return type:	tuple

initial_inf = 5¶: The number of initial infections

input_files = None¶: The set of input files that define the model Network

length_day = 0.7¶: The fraction of day considered “day” for work, e.g. 0.7 * 24 hours

static load(parameters: str = 'march29', repository: str = None, folder: str = 'parameters', filename: str = None)[source]¶

This will return a Parameters object containing all of the parameters loaded from the parameters found in file f”{repository}/{folder}/{parameters}.json”

By default this will load the march29 parameters from $HOME/GitHub/model_data/2011Data/parameters/march29.json

Alternatively, you can provide the exact path to the filename via the ‘filename’ argument

Parameters:	parameters (str) – The name of the parameters to load. This is the name that will be searched for in the METAWARDSDATA parameters directory repository (str) – The location of the cloned METAWARDSDATA repository folder (str) – The name of the folder within the METAWARDSDATA repository that contains the parameters filename (str) – The name of the file to load the parameters from - this directly loads this file without searching through the METAWARDSDATA repository
Returns:	params – The constructed and validated parameters
Return type:	Parameters

plength_day = 0.5¶: The fraction of day considered “day” for play

static read_variables(filename: str, line_numbers: List[int])[source]¶

Read in extra variable parameters from the specified line number(s) of the specified file, returning the list of the dictionaries of variables that have been read. You can then apply those variable parameters using the ‘set_variables’ function

Parameters:	filename (str) – The file from which to read the adjustable variables line_numbers (List[int]) – All of the line numbers from which to read. If this is None then all lines will be read.
Returns:	variables – The VariableSets containing all of the adjustable variables
Return type:	VariableSets

set_disease(disease: metawards._disease.Disease, silent: bool = True)[source]¶

“Set the disease that will be modelled

Parameters:	disease – The disease to be modelled. If a string is passed the disease will be loaded using that string (then) – silent – Whether or not to suppress printing out the disease

set_input_files(input_files: metawards._inputfiles.InputFiles)[source]¶

Set the input files that are used to initialise the simulation

Parameters:	input_files (InputFiles) – The set of input files that will be used to load the Network. If a string is passed then the InputFiles will be loaded based on that string.

set_variables(variables: metawards._variableset.VariableSet)[source]¶

This function sets the adjustable variable values to those specified in ‘variables’ in A COPY OF THIS PARAMETERS OBJECT. This returns the copy. It does not change this object

Parameters:	variables (VariableSet) – The variables that will be adjusted before the model run. This adjusts the parameters and returns them in a deep copy
Returns:	params – A copy of this set of parameters with the variables adjusted
Return type:	Parameters

specialised_demographics() → List[str][source]¶: Return the names of demographics that have specialised parameters that are different to those of the overall network

stage_0 = 'R'¶: how to treat the * state (stage 0). This should be a string describing the method. Currently “R”, “E” and “disable” are supported

user_params = None¶: User parameters

uv_filename = None¶: The name of the UV file